Geography
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Item Russian Winter Cropland Mapping and Impact on Land Use(2024) Abys, Christian Joseph; Skakun, Sergii Dr.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation provides an in-depth analysis of the transformation in Russia’s winter wheat industry over the past two decades, focusing on production growth, land use changes, and advancements in monitoring techniques. The study reveals a substantial 149% increase in wheat production and a 35% rise in farmland area from 2000 to 2020, driven predominantly by winter wheat, which now represents a significant portion of global exports. Despite this growth, there is notable yearly volatility in production, with USDA Foreign Agriculture Service forecasts exhibiting considerable uncertainty, particularly in area estimations which has substantial impacts on the global wheat export market. To address these challenges, the research utilizes long-term MODIS satellite data to analyze cropland expansion and intensification in southwestern Russia, identifying a 29% increase in winter wheat cropland with distinct patterns of expansion and intensification across different latitudes. The study highlights the ongoing capacity for further cropland intensification. Furthermore, this research introduces Sentinel-1 SAR imagery as an effective solution to the issue of cloud coverage, which hampers optical data accuracy. By employing various machine learning models, including multi-layer perceptron, long short-term memory, and random forest, the study demonstrates that Sentinel-1 SAR enhances the accuracy of in-season cropland mapping. The results show that Sentinel-1 SAR data reduces uncertainty in area estimations by two-thirds compared to MODIS data, offering improved monitoring capabilities. Collectively, this research provides valuable insights into Russia’s agricultural dynamics, addresses key uncertainties in forecasting, and proposes advanced methodologies for more accurate and reliable agricultural assessmentsItem Revealing the Impact of Sea Level Rise on Coastal Forest Structure in the U.S. Mid-Atlantic Using Lidar(2024) Powell, Elisabeth Brighton; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global vegetation regimes are undergoing notable changes from the effects of climate change. Coastal ecosystems, in particular are undergoing extensive shifts largely due to the compounding effects of sea level rise (SLR) and intense storm surges, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. The interplay of SLR, land subsidence, and weakened Gulf Stream exacerbates these impacts, altering the zonation from salt-tolerant marshes to upland forests. As tidal flooding increases and extends into the upland forest, elevated water and salinity levels trigger changes in ecosystem function, leading to gradual forest mortality and conversion to marshes, known as coastal transgression. However, understanding how increased tidal flooding affects forest structure and its regional variability remains limited. By leveraging lidar technology from air, land, and space, this dissertation investigates changes in low-lying forest structure induced by SLR and coastal storms, comprising three complementary studies focused on the Delaware Bay Estuary and the broader U.S. Mid-Atlantic region. First, I used multi-temporal airborne Lidar data and high-resolution imagery classify areas of rapid forest loss likely driven from episodic events in the along the Delaware Bay coast. Next, I investigated these areas of forest dieback using ground-based terrestrial laser scanning (TLS) to examine the subtle changes in forest vertical stratification from initial degradation associated with due to inundation. Lastly, I used spaceborne lidar observations to assesses the impact and extent of tidal flooding impact on forest vertical structure across the Mid-Atlantic. These studies revealed variable responses in forest structure along the forest-marsh ecotone to not only improve the delineation of the migrating forest boundary, but to also quantify the extent of degradation linked to inundation, highlighting the roles of topography and tidal influence in facilitating or resisting forest conversion into marshes. The findings of this dissertation accentuate the importance of monitoring forest structural dynamics to detect early signs of upland marsh expansion, essential for assessing changes to the overall coastal carbon sink, which underpins effective natural resource management and restoration efforts.Item EXPLORING AND ASSESSING LAND-BASED CLIMATE SOLUTIONS USING EARTH OBSERVATIONS, EARTH SYSTEM MODELS, AND INTEGRATED ASSESSMENT MODELS(2024) Gao, Xueyuan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anthropogenic greenhouse gas (GHG) emissions have led the global mean temperature to increase by approximately 1.1 °C since the industrial revolution, resulting in mass ice sheet melt, sea level rise, and an increase in extreme climate events, and exposing natural and human systems to uncertainties and the risks of unsustainable development. Meeting the Paris Agreement’s climate goal of keeping temperature increases well below 2 °C — even 1.5 °C — will require removing CO2 from the atmosphere beyond reducing GHG emissions. Therefore, carbon dioxide removal and the sustainable management of global carbon cycles are one of the most urgent society needs and will become the major focus of climate action worldwide. However, research on carbon dioxide removal remains in an early stage with large knowledge gaps. The global potential and scalability, full climate consequences, and potential side effects of currently suggested carbon sequestration options — afforestation and reforestation, bioenergy with carbon capture and storage (BECCS), direct air carbon capture — are uncertain. Moreover, although about 120 national governments have a net-zero emission target, few have actionable plans for developing carbon dioxide removal.This dissertation examines two major categories of land-based carbon removal and sequestration methods: nature-based solutions that rely on the natural carbon uptake of the land ecosystem, and technology-based solutions, especially BECCS. These two options were investigated using four studies with satellite and in-situ observations, Earth system models (climate models), and integrated assessment models (policy models). Study 1 provides evidence that land ecosystem is an important carbon sink, Study 2 assesses the carbon sequestration potential of forest sustainable management via numerical experiments, Study 3 monitors recent tropical landscape restoration efforts, and Study 4 extends to BECCS and explores the impacts of future climate changes on its efficacy. Overall, this dissertation (1) improved monitoring, reporting, and verification of biomass-based carbon sequestration efforts using Earth observations, (2) improved projections on biomass-based carbon sequestration potential using Earth system models and socio-economic models, and (3) provided guidance on scaling up biomass-based carbon sequestration methods to address the climate crisis.Item Efficient terrain analysis of point cloud datasets on a decomposition-based data representation(2024) Song, Yunting; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With a modern focus on LiDAR (Light Detection and Ranging) technologies, which generate precise three-dimensional measurements from the Earth’s surface, the amount of spatial data in the form of massive point clouds has dramatically increased. This dissertation addresses the problem of direct terrain analysis using large LiDAR point clouds without interpolating them into gridded Digital Elevation Models (DEMs). Unlike gridded DEMs, Triangulated Irregular Networks (TINs) maintain full information of point clouds and can represent terrains with variable resolution. When using TINs to represent large terrains, the major challenges are the high storage and time costs. To address these, this dissertation introduces a family of decomposition-based data structures, named Terrain trees family, for encoding TINs. A Terrain tree employs a nested subdivision strategy, partitioning the domain of the triangle mesh into several leaf blocks. Each leaf block contains the minimum amount of information required for extracting all connectivity relations that are needed for TIN navigation and terrain analysis. A new library for terrain analysis, the Terrain trees library (TTL), is developed based on the Terrain trees. Performance evaluation of TTL shows that a Terrain tree can encode the same terrain with ~36% less storagethan the state-of-art, compact data structure while maintaining good computing performance in extracting connectivity relations. Despite the highly efficient data structure, managing large TINs on local machines remains challenging, particularly for complex analyses or simulations. Mesh simplification methods are commonly applied to reduce TIN sizes to enable downstream processing. However, these simplification methods can modify the topology of the underlying terrain in an uncontrolled manner, which affects the results of terrain analysis applications. To address this issue, a topology-aware mesh simplification method based on Terrain trees is proposed. A parallel version of this simplification method is also developed, which simplifies different leaf blocks at the same time using a shared-memory implementation. A leaf-locking strategy is employed to avoid conflicts among leaf blocks during parallel computing. TTL and the topology-aware mesh simplification methods on Terrain trees effectively lower the memory and time requirements for terrain analysis on TINs. This dissertation demonstrates the effectiveness of TIN-based models in real-world applications using sea ice topography as an example. Studying sea ice topography is crucial as it enhances our ability to monitor sea ice volume changes and comprehend sea ice processes. Besides, timely and precise assessments of sea ice dynamics are critical in the context of climate change and its impacts on polar regions. TIN-based surface models are employed to represent the sea ice surface, and methods are developed for extracting important sea ice topographic features, such as density, regions without measurements, roughness, and pressure ridge structures, from TINs.Item VISUALIZATION, DATA QUALITY, AND SCALE IN COMPOSITE BATHYMETRIC DATA GENERALIZATION(2024) Dyer, Noel Matarazza; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Contemporary bathymetric data collection techniques are capable of collecting sub-meterresolution data to ensure full seafloor-bottom coverage for safe navigation as well as to support other various scientific uses of the data. Moreover, bathymetry data are becoming increasingly available. Datasets are compiled from these sources and used to update Electronic Navigational Charts (ENCs), the primary medium for visualizing the seafloor for navigation purposes, whose usage is mandatory on Safety Of Life At Sea (SOLAS) regulated vessels. However, these high resolution data must be generalized for products at scale, an active research area in automated cartography. Algorithms that can provide consistent results while reducing production time and costs are increasingly valuable to organizations operating in time-sensitive environments. This is particularly the case in digital nautical cartography, where updates to bathymetry and locations of dangers to navigation need to be disseminated as quickly as possible. Therefore, this dissertation covers the development of cartographic constraint-based generalization algorithms operating on both Digital Surface Model (DSM) and Digital Cartographic Model (DCM) representations of multi-source composite bathymetric data to produce navigationally-ready datasets for use at scale. Similarly, many coastal data analysis applications utilize unstructured meshes for representing terrains due to the adaptability, which allows for better conformity to the shoreline and bathymetry. Finer resolution along narrow geometric features, steep gradients, and submerged channels, and coarser resolution in other areas, reduces the size of the mesh while maintaining a comparable accuracy in subsequent processing. Generally, the mesh is constructed a priori for the given domain and elevations are interpolated to the nodes of the mesh from a predefined digital elevation model. These methods can also include refinement procedures to produce geometrically correct meshes for the application. Mesh simplification is a technique used in computer graphics to reduce the complexity of a mesh or surface model while preserving features such as shape, topology, and geometry. This technique can be used to mitigate issues related to processing performance by reducing the number of elements composing the mesh, thus increasing efficiency. The primary challenge is finding a balance between the level of generalization, preservation of specific characteristics relevant to the intended use of the mesh, and computational efficiency. Despite the potential usefulness of mesh simplification for reducing mesh size and complexity while retaining morphological details, there has been little investigation regarding the application of these techniques specifically to Bathymetric Surface Models (BSMs), where additional information such as vertical uncertainty can help guide the process. Toward this effort, this dissertation also introduces a set of experiments that were designed to explore the effects of BSM mesh simplification on a coastal ocean model forced by tides in New York Harbor. Candidate vertices for elimination are identified using a given local maximum distance between the original vertices of the mesh and the simplified surface. Vertex removal and re-triangulation operations are used to simplify the mesh and are paired with an optional maximum triangle area constraint, which prevents the creation of new triangles over a specified area. A tidal simulation is then performed across the domain of both the original (un-simplified) and simplified meshes, while comparing current velocities, velocity magnitudes, and water levels over time at twelve representative locations in the Harbor. It was demonstrated that the simplified mesh derived from using even the strictest parameters for the mesh simplification was able to reduce the overall mesh size by approximately 26.81%, which resulted in a 26.38% speed improvement percentage compared to the un-simplified mesh. Reduction of the overall mesh size was dependent on the parameters for simplification and the speed improvement percentage was relative to the number of resulting elements composing the simplified mesh.Item Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion(2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.Item Characterizing the Multi-scale Post-fire Forest Structural Change in North American Boreal Forests using Air- and Space-borne Lidar Observations(2024) Feng, Tuo; Duncanson, Laura; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wildfire is the dominant stand-replacing disturbance regime in boreal North America, shaping the pattern, structure and composition of forested landscapes. Forest losses and gains through wildfires are two linked ecological processes, despite their varied functionalities in terrestrial carbon budgets. Combustion of forest biomass through wildfires results in the release of terrestrial carbon, whereas subsequent forest recovery process would re-sequestrate atmospheric CO2 back to the plants, and therefore at least partially offsets fire-induced carbon emissions. However, the magnitude of forest carbon fluxes and its association with wildfires is highly uncertain, especially under the context of large anomalies in fire regimes during the past few decades due to climate change. To fill the knowledge gaps, this dissertation focuses on integrations of air- and space-borne Light Detection and Ranging (lidar) to assess the magnitudes of forest structure and Aboveground Biomass Density (AGBD) changes with respect to wildfires. This dissertation starts with a systematic evaluation of multi-resolution Ice, Cloud and land Elevation Satellite -2 (ICESat-2) terrain and canopy height estimates over boreal North America. As one of the first ICESat-2 validation studies, this work demonstrates ICESat-2 as a suitable platform for large-scale terrain and canopy height measurements, and further provides a suite of standards for ICESat-2 data filtering over boreal forests. Thereafter, I analyze magnitude of forest structure and AGBD changes through wildfire events with multi-temporal airborne lidar and Landsat. This study establishes quantitative linkages between multispectral and structural measurements of fire effects on forest damage, and further reveals burn severity levels, pre-fire forest structure and fire-return intervals as dominant drivers for the magnitude of forest damage through fires. Finally, this dissertation investigates continental-scale forest recovery rate through a full-collection of high-resolution ICESat-2 observations, Landsat-based disturbance history and multi-decadal climatology records. The forest recovery rates under different warming trend are found to be converging over the past few decades, demonstrated as the growth rate of forests across high-latitudinal North gradually approaching their counterparts over Southern boreal zones. This work further reveals a positive effect of growing season warming on forest deciduousness shift, and concludes that regions with warming and associated increase in deciduous compositions would experience greatest growth rate acceleration. This dissertation leverages the potential of multi-sourced remote sensing datasets to assess spatial extents, magnitudes, and underlying drivers of forest carbon feedbacks to climate change and wildfires over North American boreal ecosystem.Item DEEP LEARNING APPROACHES FOR ESTIMATING AND FORECASTING SURFACE DOWNWARD SHORTWAVE RADIATION FROM SATELLITE DATA(2024) Li, Ruohan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Surface downward shortwave radiation (DSR) designates solar radiation with a wavelength from 300 to 4000 nm received at the Earth’s surface. DSR plays a pivotal role in the surface energy and radiation budget, serving as the primary driver for hydrological, ecological, and biogeochemical cycles (Liang et al., 2010, 2019), and the important input for various earth models (Huang et al., 2019; Liang et al., 2010; Stephens et al., 2012). Given the rising demand for renewable energy, as well as accelerated advancements in solar energy technologies on both utility-scale and residential scale, the precision and resolution in estimating and forecasting DSR have become indispensable for planning and administering solar power plants (Gueymard, 2014; Jiang et al., 2019). This dissertation delves into the potential of integrating deep learning with satellite observations to address the deficiencies in current DSR estimation and forecasting methods, aiming to cater to the evolving needs of solar radiation estimation. The research begins by examining current DSR satellite products, emphasizing their limitations, particularly concerning spatial resolution and performance in snowy, cloudy, and high-latitude areas. In such regions, challenges arise from the degradation of radiative transfer models, band saturation, the pronounced effects of 3D cloud dynamics, and temporal resolution constraints (Li et al., 2021). Identifying these gaps, the study introduces the concept of transfer learning to tackle cases where physical methods degrade and limited training data is available. By combining data from physical simulations and ground observations, the proposed models enhance both the accuracy and adaptability of DSR predictions on a global scale. The investigation further reveals the influence of training data volume on model performance, illustrating how transfer learning can ameliorate these effects (Li et al., 2022). Moreover, the dissertation compares the application of DenseNet, Gated Recurrent Unit (GRU), and a hybrid of Convolutional Neural Network (CNN) and GRU (CNNGRU) to geostationary satellite data, achieving precise and timely DSR estimates. These models underscore their prowess in tackling 3D cloud effects and reducing dependency on additional data sources by the spatial and temporal structure of DL (Li et al., 2023b). Finally, the dissertation introduces the SolarFormer, a space-time transformer neural network adept at forecasting solar radiation up to three hours in advance at 15-minute intervals. By harnessing solely geostationary satellite imagery without the need for ground measurements, this model facilitates expansive DSR predictions, which are crucial for optimizing solar energy distribution at both utility and micro scales. This chapter also highlights the Transformer model's potential for extended forecasting due to its computational and memory efficiency.Item TOPOLOGY-BASED INDIVIDUAL TREE MAPPING FROM LIDAR POINT CLOUDS(2024) Xu, Xin; De Floriani, Leila LDF; Iuricich, Federico FI; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Light Detection and Ranging (LiDAR) techniques have dramatically enhanced our ability to characterize forest structures remotely by acquiring 3D point cloud samplings of forest shapes. Extracting individual trees from the forests plays a critical role in the automated processing pipeline of forest point cloud analysis. However, there is still a lack of automated, efficient, and easy-to-use approaches available to identify and extract individual trees in a forest point cloud. This is mainly due to inconsistent point cloud quality, diverse forest structure, and complicated plant morphology. Most existing methods require intensive parameter tuning, time-consuming user interactions, and external information (i.e., allometric function). In this dissertation, we consider the problem of extracting single-tree point clouds from input forest point clouds. We propose two novel Topology-based Tree Segmentation (TTS) approaches, namely TTS-ALS and TTS-TLS, for airborne and terrestrial laser scanning data analysis, respectively. TTS algorithms are plug-and-play by nature and controlled by at most one parameter, ensuring user-friendliness. The implemented TTS software tools can extract single trees from 3D point clouds on various forest types, including conifer trees, broadleaf deciduous forests, and evergreen subtropical trees. Compared to state-of-the-art software tools, TTS tools achieve more accurate stem localization and tree extraction results on a broad set of forest types and point densities. Further experiments show that point normalization, one preprocessing step before TTS, slightly affects the TTS-ALS's performance of detected tree locations while strongly influencing tree crowns. Compared to TTS-ALS, TTS-TLS segmentation accuracy is more sensitive to normalized points. However, TTS-TLS can effectively limit errors introduced by the preprocessing step in local regions and maintain consistent results across entire areas. Because of their reliability and generality, TTS approaches are promising for ample usage of forest LiDAR point clouds in forestry and ecology studies, such as automated forest inventory generation from point clouds. Additionally, our extra research includes a novel building footprint delineation method for ALS point clouds and a comprehensive review of tree reconstruction methods tailored to single-tree point clouds, enhancing the breadth and depth of our contribution.Item INTEGRATED MONITORING OF DISTURBANCE AND FOREST ATTRIBUTES(2024) Lu, Jiaming; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests provide numerous ecosystem services and are shaped by historical disturbance events. The intensity of disturbance significantly influences the post-disturbance forest structure, species composition, and subsequent forest regrowth. Under the influence of anthropogenic activities and climate change, disturbance regime has undergone unprecedent changes and is subsequently affecting a suite of interrelated forest attributes that are critical in understanding forests dynamics. Historical large-scale disturbance intensity information is needed for understanding the change in disturbance regimes and create linkage to forest dynamics, but such dataset was not available. Forest attributes can be estimated from the spectral information of remote sensing imagery; however, inconsistency exists among the developed product, and the usage of the dataset is limited by accuracy. To fill the research gaps, this dissertation aims to develop a framework that integrates the historical disturbance and the inter-relationship between forest attributes to provide more consistent, and likely more accurate forest attribute estimations. Age, a key attribute that can be the determinant of many ecosystem processes and tree/forest stand develop stage, was selected as the prototype attribute to study. The dissertation started by producing the first set of annual forest disturbance intensity map products quantifying thepercentage of basal area removal (PBAR) at the 30-m resolution for the CONUS from 1986 to 2015, by integrating field plot measurements collected by the Forest Inventory and Analysis program with time series Landsat observations. Compared to other published disturbance products, the maps derived through this study can provide the unique thematic (intensity) information on forest disturbances, precise details critical for understanding forest dynamics across CONUS over multiple decades. The dissertation then proceeded to quantify individual tree age. The tree age was estimated for every tree in the FIA database (over 10 million trees) across the US from our modeling approach that had higher accuracies than existing studies. The developed tree age dataset allows better characterization of tree age distribution, which is important for understanding the disturbance history, functioning, and growth vigor of forest ecosystems. With the disturbance intensity and tree age dataset, the dissertation was able to develop an integrated modeling approach for the forest age mapping. The forest age and complexity maps were produced for 2015 and 2005. The combination of the two metrics should provide a more comprehensive characterization of the forest development condition. The maps provide valuable information for knowing forest conditions, estimating forest growth and carbon sequestration potential, understanding the relationship between age and other forest attributes, evaluating forest health, and planning sustainable forest management practices. This modeling framework developed by the dissertation will enhance the ability to retrieve forest attributes in a broader scale so that with the remote sensing observation, we can not only provide spatially explicit forest structure information, but also review forest status over the decades. Furthermore, when combined with the ecosystem models, these estimations will provide a better prediction for future vegetation and climate dynamics.